Tuva and Education Research

Explore the educational research and publications inform the design and development of Tuva.

Why Teach with Data? Why Support Data Literacy?

Tuva emphasizes the value of data literacy for improving not just data, statistics, or analytical skills, but for learning subject content, developing inquiry skills, inspiring curiosity, and building language and thought patterns for reasoning about evidence when making sense of data.


The tools, pedagogy, and framework for teaching data literacy that form the backbone of Tuva are supported by current education research. Here are a few of the research publications that inform the development of Tuva.

Data Literacy is Interdisciplinary

Myriad online sources of data can provide students with real evidence that supports learning subject content in science, mathematics, history, social sciences, language arts, and the arts.


  1. English, Lyn D. 2015. “STEM: Challenges and Opportunities for Mathematics Education.” In Proceedings of the 39th Conference of the International Group for the Psychology of Mathematics Education,, 4–18. Queensland University of Technology.
  2. Newman, Mark, Sophie Degener, and Ziuwen Wu. 2015. “How Are Teachers Using Primary Sources to Meet Common Core Literacy Standards in English/ Language Arts, Social Studies, and Science?.” NCE Research Residencies, Digital Commons, October, 1–11.
  3. Krajcik, Katherine L McNeill Joseph. 2005. “Middle School Students’ Use of Appropriate and Inappropriate Evidence in Writing Scientific Explanations.”
  4. Finnis, Joel, Atanu Sarkar, and Mark C J Stoddart. 2015. “Bridging Science and Community Knowledge? the Complicating Role of Natural Variability in Perceptions of Climate Change.” Global Environmental Change 32 (May).

Data Literacy is Student-Centered

Research-supported pedagogy for building data literacy scaffolds students towards open-ended reasoning, applying quantitative analysis in inquiry, and visualizing, describing and making sense of variability when interpreting “messy” real-world data. Exploratory Data Analysis precedes Confirmatory (Quantitative) Data Analysis.


  1. Bakker, Arthur. 2004. “Reasoning About Shape as a Pattern in Variability.” Statistics Education Research Journal 3 (2): 64–83.
  2. Pfannkuch, Maxine. 2006. “Special Topic Issue: Students' Understanding of Variability and Distribution.” Statistics Education Research Journal, December, 1–127.
  3. Shaughnessy, J Michael. 2006. “Student Work and Student Thinking: an Invaluable Source for Teaching and Research.” Icots-7. ICOTS-7.
  4. Pfannkuch, Maxine, Matt Regan, Chris Wild, and Nicholas J Horton. 2010. “Telling Data Stories: Essential Dialogues for Comparative Reasoning,” July, 1–38.
  5. Ekol, George. 2015. “Exploring Foundation Concepts in Introductory Statistics Using Dynamic Data Points.” International Journal of Education in Mathematics, Science and Technology (IJEMST) 3 (3): 230–41.
  6. Arnold, Pip, and Maxine Pfannkuch. 2012. “The Language of Shape.” 12th International Congress on Mathematical Education, April, 1–10.
  7. Zoellick, B., Schauffler, M., Flubacher, M., Weatherbee, R, and Webber, H., 2015. Data Literacy: Assessing Student Understanding of Variability in Data. Presented at the Annual Meeting of the National Association for Research in Science Teaching, April 14-17, 2016, Baltimore, MD.
  8. Harris, Cornelia, Alan Berkowitz, and Angelita Alvarado. 2012. “Data Explorations in Ecology: Salt Pollution as a Case Study for Teaching Data Literacy.” The American Biology Teacher 74 (7): 479–84. doi:10.1525/abt.2012.74.7.9.
  9. Laursen, Sandra; Anne-Barrie Hunter; Elaine Seymour; and Heather Thiry. 2010. Undergraduate Research in the Sciences: Engaging Students in Real Science. San Francisco, CA: Jossey-Bass.
  10. Pfannkuch, Maxine. 2011. “The Role of Context in Developing Informal Statistical Inferential Reasoning: a Classroom Study.” Mathematical Thinking and Learning 13 (1-2): 27–46.
  11. Neuman, David L, Michelle Hood, and Michelle M Neuman. 2013. “Using Real-Life Data When Teaching Statistics: Student Perceptions of This Strategy in an Introductory Statistics Course.” Statistics Education Research Journal 12 (2): 59–70.
  12. Moss DM, Abrams ED, and Kull JA. 1998. Can we be scientists too? Secondary students’ perceptions of scientific research from a project-based classroom. J Sci Educ Technol 7: 146–61.
  13. McNeill, Katherine L, and Joseph Krajcik. 2006. “Supporting Students’ Construction of Scientific Explanation Through Generic Versus Context- Specific Written Scaffolds.” Paper presented at the annual meeting of the American Educational Research Association, April, 2006, San Francisco.

Authentic Data Motivates Students and Can Improve Content Understanding

  1. Sweet, Stephen, Susanne Morgan, and Danette Ifert Johnson. 2008. “Using Local Data to Advance Quantitative Literacy.” Numeracy 1 (2): 1–23.
  2. Harris, Cornelia, Alan Berkowitz, and Angelita Alvarado. 2012. “Data Explorations in Ecology: Salt Pollution as a Case Study for Teaching Data Literacy.” The American Biology Teacher 74 (7): 479–84. doi:10.1525/abt.2012.74.7.9.
  3. Laursen, Sandra; Anne-Barrie Hunter; Elaine Seymour; and Heather Thiry. 2010. Undergraduate Research in the Sciences: Engaging Students in Real Science. San Francisco, CA: Jossey-Bass.
  4. Pfannkuch, Maxine. 2011. “The Role of Context in Developing Informal Statistical Inferential Reasoning: a Classroom Study.” Mathematical Thinking and Learning 13 (1-2): 27–46.
  5. Neuman, David L, Michelle Hood, and Michelle M Neuman. 2013. “Using Real-Life Data When Teaching Statistics: Student Perceptions of This Strategy in an Introductory Statistics Course.” Statistics Education Research Journal 12 (2): 59–70.
  6. Moss DM, Abrams ED, and Kull JA. 1998. Can we be scientists too? Secondary students’ perceptions of scientific research from a project-based classroom. J Sci Educ Technol 7: 146–61.
  7. McNeill, Katherine L, and Joseph Krajcik. 2006. “Supporting Students’ Construction of Scientific Explanation Through Generic Versus Context- Specific Written Scaffolds.” Paper presented at the annual meeting of the American Educational Research Association, April, 2006, San Francisco.

Technology Tools Can Facilitate Deeper Reasoning About Data

  1. Konold, Clifford, Traci Higgins, Susan Jo Russell, and Khalimahtul Khalil. 2014. “Data Seen Through Different Lenses.” Educational Studies in Mathematics 88 (3): 305–25. doi:10.1007/s10649-013-9529-8.
  2. Biehler, Rolf, Dani Ben-Zvi, Arthur Bakker, and Katie Makar. 2012. “Technology for Enhancing Statistical Reasoning at the School Level.” In Technology for Enhancing Statistical, 643–89. New York, NY: Springer New York.
  3. Bakker, Arthur, Rolf Biehler, and Clifford Konold. 2004. “Should Young Students Learn About Box Plots?.” In Curricular Development in Statistics Education, 1–11.
  4. Webber, H., Nelson, S.J., Weatherbee, R. Zoellick, B., and Schauffler, M, 2014. The Graph Choice Chart: A tool to help students turn data into evidence. The Science Teacher, November 2014: 37-43 (National Science Teacher’s Association).
  5. Konold, C. (2007). Designing a data tool for learners. In M. Lovett & P. Shah (Eds.), Thinking with data (pp. 267-291). New York: Taylor & Francis.